In malware research, threat hunting and sharing of threat intelligence, such as exchanging indicators of compromise (IoCs) in the form of hashes (e.g., MD5s, SHA256s), are common industry practices and helpful for information security professionals. Researchers, for instance, would typically search for malware samples on VirusTotal using hashes. However, hashes have some characteristics that could limit researchers trying to do file or threat correlation, such as the one-to-one relationship between a file and its hash. To overcome limitations, other hashing techniques, methodologies, and tools have been proposed, such as ssdeep, sdhash, imphash, and even our own Trend Micro Locality Sensitive Hashing (TLSH) and they can indeed help researchers find and identify the similarities between binary files. These approaches use binary as a point of view.

Our research, which weve named Graph Hash, builds on the advantages of these two approaches by calculating the hash of executable files using a graph view, which would help in classifying malware more consistently and efficiently. Our research aims to provide a viable approach to malware classification, which, in turn, can help in the sharing of actionable threat intelligence beyond simple checksums, such as MD5s and secure hash algorithm (SHA) families.